


'''simple docstring'''



def      _UpperCamelCase   (		UpperCamelCase__       ):
			UpperCAmelCase__	:       List[Any]   =  [False] * len(UpperCamelCase__       )
			UpperCAmelCase__	:       Dict   =  [-1] * len(UpperCamelCase__       )

			def dfs(UpperCamelCase__			,   UpperCamelCase__       ):
						UpperCAmelCase__	:       List[str]   =  True
						UpperCAmelCase__	:       List[str]   =  c
						for u in graph[v]:
									if not visited[u]:
												dfs(UpperCamelCase__			,   1 - c       )

			for i in range(len(UpperCamelCase__       )       ):
						if not visited[i]:
									dfs(UpperCamelCase__			,   0       )

			for i in range(len(UpperCamelCase__       )       ):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
__A						={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))





'''simple docstring'''







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
    import torch.distributed.checkpoint as dist_cp
    from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
    from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowerCAmelCase_     						=				get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      A__       =     model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         A__       =     F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""						)
            torch.save(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Model saved to {output_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         A__       =     (
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Saving model to {output_model_file}"""						)
         torch.save(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Model saved to {output_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         A__       =     os.path.join(UpperCAmelCase      ,F"""{MODEL_NAME}_{model_index}"""						)
         os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
         logger.info(F"""Saving model to {ckpt_dir}"""						)
         A__       =     {'model': state_dict}

         dist_cp.save_state_dict(
             state_dict=UpperCAmelCase      ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase						)      ,planner=DefaultSavePlanner()      ,)
         logger.info(F"""Model saved to {ckpt_dir}"""						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if type(UpperCAmelCase						) != FSDP and accelerator.process_index != 0:
            if not fsdp_plugin.sync_module_states:
               raise ValueError(
                   'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
                   'initializing FSDP object'						)
            return
         A__       =     F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading model from {input_model_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Model loaded from {input_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         A__       =     (
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading model from {input_model_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Model loaded from {input_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         A__       =     (
             os.path.join(UpperCAmelCase      ,F"""{MODEL_NAME}_{model_index}"""						)
             if F"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading model from {ckpt_dir}"""						)
         A__       =     {'model': model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=UpperCAmelCase      ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase						)      ,planner=DefaultLoadPlanner()      ,)
         A__       =     state_dict['model']
         logger.info(F"""Model loaded from {ckpt_dir}"""						)
      model.load_state_dict(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      A__       =     FSDP.optim_state_dict(UpperCAmelCase      ,UpperCAmelCase						)
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            A__       =     (
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""						)
            torch.save(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""						)
      else:
         A__       =     os.path.join(UpperCAmelCase      ,F"""{OPTIMIZER_NAME}_{optimizer_index}"""						)
         os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""						)
         dist_cp.save_state_dict(
             state_dict={'optimizer': optim_state}      ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase						)      ,planner=DefaultSavePlanner()      ,)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         A__       =     None
         # below check should work but currently it isn't working (mostly opytorch issue),
         # in the meantime disabling it at the cost of excess memory usage
         # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
         A__       =     (
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""						)
      else:
         A__       =     (
             os.path.join(UpperCAmelCase      ,F"""{OPTIMIZER_NAME}_{optimizer_index}"""						)
             if F"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading Optimizer from {ckpt_dir}"""						)
         A__       =     load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()      ,optimizer_key='optimizer'      ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase						)      ,)
         A__       =     optim_state['optimizer']
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""						)
      A__       =     FSDP.optim_state_dict_to_load(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
      optimizer.load_state_dict(UpperCAmelCase						)


